ACS Map Code Sample
Cassandra T-Pederson
2025-02-05
Purpose
Create maps to visualize median household income across New York City using ACS data.
Set-up
# Enable caching of data
options(tigris_use_cache = TRUE)Packages
# Loading packages
library(rmarkdown) # markdown package to help knit to git
library(dplyr) # for data manipulation
library(purrr) # map list & other functions
library(ggplot2) # for graphing
library(sf) # simple features - encoding spatial vector data
library(svglite) # for exporting SVG files
library(stringr) # text/string functions
library(shiny) # interactive dashboard
library(tidycensus) # tidyverse census functions
library(tigris) # read census shapefiles
library(leaflet) # interactive maps
library(ggiraph) # interactive maps via ggplot2Census API key
In order to access the Census API, you need to enter a Census API key, which you can request here: https://api.census.gov/data/key_signup.html
# Use function to install your API key for future sessions. This will add your CENSUS API key to your .Renviron file so it can be called securely without being stored in your code.
census_api_key("[INSERT YOUR KEY]", install = TRUE)
# Alternatively, access the API by entering in your API key in quotes below just once each session
Sys.setenv(CENSUS_API_KEY = "[INSERT YOUR KEY]")# After you have installed your key, it can be called any time by typing Sys.getenv("CENSUS_API_KEY") and can be used in package functions by simply typing CENSUS_API_KEY
Sys.getenv("CENSUS_API_KEY")Load ACS Data
Will use the 5-year estimate data from 2015 for median household income to start.
Output variable
# Load variables from ACS5 2015 census and isolate the variable for median household income
vacs15 <- load_variables(2015, "acs5", cache = T)
# Confirm this is the correct outcome variable needed
vacs15 %>%
filter(str_detect(name, "B19113_001"))## # A tibble: 1 × 4
## name label concept geography
## <chr> <chr> <chr> <chr>
## 1 B19113_001 Estimate!!Median family income in the past 12 mo… MEDIAN… tract# Good - label confirms this is the correct variable Income from ACS5 by Census Tract Table
Load income data by tract and subset to the 5 NYC boroughs.
incmedhh_2015 <- get_acs(geography = "tract",
variables = c(incmedhh = "B19113_001"),
state = "NY",
county = c("New York",
"Kings",
"Queens",
"Bronx",
"Richmond County"),
year = 2015,
geometry = T)
# Preview df
head(incmedhh_2015)## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -73.92922 ymin: 40.80354 xmax: -73.87251 ymax: 40.8299
## Geodetic CRS: NAD83
## GEOID NAME variable estimate moe
## 1 36005002300 Census Tract 23, Bronx County, New York incmedhh 17671 6565
## 2 36005002701 Census Tract 27.01, Bronx County, New York incmedhh 24784 6865
## 3 36005004100 Census Tract 41, Bronx County, New York incmedhh 18671 6541
## 4 36005004800 Census Tract 48, Bronx County, New York incmedhh 31538 6814
## 5 36005006700 Census Tract 67, Bronx County, New York incmedhh 21747 5670
## 6 36005008900 Census Tract 89, Bronx County, New York incmedhh 30385 5751
## geometry
## 1 MULTIPOLYGON (((-73.92922 4...
## 2 MULTIPOLYGON (((-73.91796 4...
## 3 MULTIPOLYGON (((-73.92468 4...
## 4 MULTIPOLYGON (((-73.8764 40...
## 5 MULTIPOLYGON (((-73.92178 4...
## 6 MULTIPOLYGON (((-73.89704 4...Static map with ggplot2
Get shapefile for NYC water layer. Will use as separate layer for map formatting.
nyc_water <- area_water("NY", c("New York",
"Kings",
"Queens",
"Bronx",
"Richmond County"))Plot data using ggplot2
ggplot(incmedhh_2015) +
# Income data
geom_sf(alpha = 1,
color = NA,
aes(fill = estimate)) +
# Theme removes cartographic background grid & axis labels
theme_void() +
# Legend
scale_fill_viridis_c(labels = scales::label_dollar(),
name = "Median Household Income") +
# Remove water layer by setting color to white
geom_sf(data = nyc_water,
fill = "white",
color = NA,
aes(geometry = geometry)) +
# Set background to white to match water layer
theme(panel.background = element_rect(fill = "white",
color = NA)) 
Interactive maps
Create interactive maps using leaflet and ggiraph packages
leaflet
# Set color palette argument
pal <- colorNumeric(
palette = "magma",
domain = incmedhh_2015$estimate
)
# Plot
leaflet() %>%
#providers argument sets map background. Chose one of the free versions available through leaflet
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(data = incmedhh_2015,
color = ~pal(estimate),
weight = 0.5,
smoothFactor = 0.2,
fillOpacity = 0.5,
label = ~estimate)ggiraph
ggiraph is a package that makes ggplot2 figures interactve. Use the basic code set up from the original static ggplot2 map for this section and edit as needed.
# Create new df that has a text column combining each tract name and the income amount (i.e. "[TRACT NAME]: [$ AMOUNT]"). This will appear when cursor hovers over each tract
incmedhh_2015_ggir <- incmedhh_2015 %>%
mutate(tooltip = paste(NAME, estimate, sep = ": "))
# Plot with ggiraph
ggir <- ggplot(incmedhh_2015_ggir, aes(fill = estimate)) +
# Follow same set up as ggplot2 static map, but with interactive version of ggplot2 functions
geom_sf_interactive(aes(tooltip = tooltip, data_id = NAME),
size = 0.1) +
scale_fill_viridis_c_interactive(option = "plasma",
labels = scales::label_dollar()) +
# Color water layer white
geom_sf(data = nyc_water,
fill = "white",
color = NA,
aes(geometry = geometry)) +
labs(title = "Median Household Income by Census Tract",
caption = "Data source: 2015 5-year ACS, US Census Bureau",
fill = "ACS estimate") +
# Theme removes cartographic background grid & axis labels
theme_void() +
# Set background white to match water layer
theme(panel.background = element_rect(fill = "white",
color = NA))
# Output
girafe(ggobj = ggir) %>%
# Fill tract with select color when cursor hovers over it
girafe_options(opts_hover(css = "fill:cyan;"),
opts_zoom(max = 10))Multi-year interactive map
Create interactive map that uses data from multiple years from the ACS-5 survey
# Save years of interest to list
years <- lst(2010, 2015, 2020)
# Run function to load census data across years in list
incmedhh_all <- map_dfr(
years,
~ get_acs(
geography = "tract",
variables = "B19113_001",
state = "NY",
county = c("New York",
"Kings",
"Queens",
"Bronx",
"Richmond County"),
year = .x,
survey = "acs5",
geometry = T
),
.id = "year"
) %>%
mutate(year = as.numeric(year))Shiny
Create interactive dashboard with Shiny to look at multi-year data using ggiraph package
# Create text column for hover over feature
incmedhh_all_ggir <- incmedhh_all %>%
mutate(tooltip = paste(NAME, estimate, sep = ": "))# Shiny setup
ui <- fluidPage(
# Create sidebar with dropdown menu to select year
sidebarLayout(
sidebarPanel = sidebarPanel(
selectInput(
'selected_year',
'Select a year to map',
choices = unique(incmedhh_all_ggir$year)
)
),
mainPanel = mainPanel(
girafeOutput('girafe_output', height = 600)
)
)
)
server <- function(input, output, session) {
# Reactive function that filters by year based on input
dat_year <- reactive({incmedhh_all_ggir |>
filter(year == input$selected_year)})
output$girafe_output <- renderGirafe({
# ggiraph/ggplot2 code (same setup as the previous sections)
ggir_all <- ggplot(data = dat_year(),
aes(fill = estimate)) +
geom_sf_interactive(aes(tooltip = tooltip, data_id = NAME),
size = 0.1) +
scale_fill_viridis_c_interactive(option = "plasma",
labels = scales::label_dollar()) +
# Color water layer white
geom_sf(data = nyc_water,
fill = "white",
color = NA,
aes(geometry = geometry)) +
labs(title = "Median Household Income by Census Tract",
caption = "Data source: 5-year ACS, US Census Bureau",
fill = "ACS estimate") +
# Theme removes cartographic background grid & axis labels
theme_void() +
# Set background white to match water layer
theme(panel.background = element_rect(fill = "white",
color = NA))
girafe(ggobj = ggir_all,
options = list(opts_hover(css = "fill:cyan"),
opts_zoom(max = 10)))
})
# Shiny session runs until you end it by closing pop up window
session$onSessionEnded(function() {
stopApp()
})
}
shinyApp(ui = ui, server = server)Shiny applications not supported in static R Markdown documents